ArticleScience (New York, N.Y.)2021
Accurate prediction of protein structures and interactions using a three-track neural network.
Article in Science (New York, N.Y.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2,734 papers.
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2,734 citing papers in PubMed.
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- Tryptoline stereoprobe elaboration identifies inhibitors of the GRPEL1-HSPA9 complex.Nature chemical biology · 2026Article
- LoL-align: sensitive and fast probabilistic protein structure alignment.Nature methods · 2026Article
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- DESIGN: a framework for generative AI-enabled crop molecular design-driven breeding.Science China. Life sciences · 2026Review
- Advancing genetic pathogenicity prediction with three-dimensional proteoform-phenotype analysis.medRxiv : the preprint server for health sciences · 2026Article
- Graph-based representations in modern protein science.The Biochemical journal · 2026Review
- A structure-aware generative AI framework for revealing functional relationships in protein families.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Antibody-drug conjugate engineering: from design to efficacy and safety.Signal transduction and targeted therapy · 2026Review
2,674 more citing papers are in PubMed but not listed here.
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32 authors.
Funding
Abstract
DeepMind presented notably accurate predictions at the recent 14th Critical Assessment of Structure Prediction (CASP14) conference. We explored network architectures that incorporate related ideas and obtained the best performance with a three-track network in which information at the one-dimensional (1D) sequence level, the 2D distance map level, and the 3D coordinate level is successively transformed and integrated. The three-track network produces structure predictions with accuracies approaching those of DeepMind in CASP14, enables the rapid solution of challenging x-ray crystallography and cryo-electron microscopy structure modeling problems, and provides insights into the functions of proteins of currently unknown structure. The network also enables rapid generation of accurate protein-protein complex models from sequence information alone, short-circuiting traditional approaches that require modeling of individual subunits followed by docking. We make the method available to the scientific community to speed biological research.
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